Matplotlib Animated Charts in Python

   OpenCV Python Projects with Source Code      https://lnkd.in/dJazY8jE Contents… 1 Python Programs, to explain cv2, to Capture Video, Create face mesh 2 Python Programs, Flip image horizontally & bluid Face & Hand ... 3 Python programs, to Build Hand & Body Tracking System                                                           

🐍📰 Use Sentiment Analysis With Python to Classify Movie Reviews In this tutorial, you'll then build your own sentiment analysis classifier with spaCy that can predict whether a movie review is positive or negative #python

🐍📰 Splitting, Concatenating, and Joining Strings in Python In this beginner-friendly article, you’ll learn some of the most fundamental string operations: splitting, concatenating, and joining. #python

🐍📰 In this tutorial, you'll learn how to use NumPy's np.linspace() effectively to create an evenly or non-evenly spaced range of numbers. You'll explore several practical examples of the function's many uses in numerical applications. #python

Consider the following code in Python: def add_item(lst): lst.append(100) a = [1, 2, 3] add_item(a) print(a) What happens here? The correct explanation is: ✅ An in-place modification occurs on the list. Lists in Python are mutable objects, which means they can be modified after they are created. Let’s break it down step by step. 1️⃣ Creating the list When we write: a = [1, 2, 3] Python creates a list object in memory, and the variable a references it: a → [1, 2, 3] 2️⃣ Calling the function When the function is called: add_item(a) The parameter lst inside the function now references the same list object: a → [1, 2, 3] lst → ↑ (same list) ➡️ Both variables point to the same object in memory. 3️⃣ Inside the function Inside the function we execute: lst.append(100) The append() method modifies the list itself. This is called in-place modification, meaning the original list object is updated instead of creating a new one. The list now becomes: [1, 2, 3, 100] 4️⃣ Printing the result Since both a and lst reference the same list, the change is visible through a. Now when we execute: print(a) Output: [1, 2, 3, 100] 📌 Final thought Understanding how variables reference objects in memory is essential when working with mutable data types like lists in Python. #Python #PythonProgramming #Coding #LearnPython #SoftwareDevelopment

Quick Python Challenge What will be the output of this code? a = [1, 2] b = a b.append(3) print(len(a)) Options: A) 2 B) 3 C) 1 D) Error 💡 At first glance, many people think the answer is 2. But the correct answer is actually 3. Why? Because in Python: b = a does not create a new list. It simply makes b reference the same object in memory as a. So when we run: b.append(3) we are modifying the same list that both a and b point to. The list becomes: [1, 2, 3] So: len(a) = 3 📌 Key Insight: In Python, variables can reference the same mutable object, which means modifying one reference affects the other. 🔥 Lesson of the day: Understanding mutable objects and references is essential when writing reliable Python code—especially in data analysis and AI pipelines. 💬 Curious: Did you get it right on the first try? #Python #AI #DataAnalytics #LearningInPublic #30DayChallenge #PythonTips

💬 Discussion Question: In Python, we often hear about variable scope. But what does that actually mean? In simple terms, scope determines where a variable can be accessed in a program. There are two common types developers deal with: 🔹 Local Variables These are variables defined inside a function. They only exist within that function and cannot be accessed outside of it. 🔹 Global Variables These are defined outside functions and can be accessed throughout the program. But Python actually follows a specific rule when searching for variables called the LEGB Rule: L → Local (inside the current function) E → Enclosing (inside outer functions, in case of nested functions) G → Global (variables defined at the top level of the script) B → Built-in (Python’s built-in names like "len", "print", etc.) Python checks these scopes in this exact order when resolving a variable name. ⚠️ In larger programs, it's usually better to avoid relying heavily on global variables. They can make debugging harder, create unexpected side effects, and reduce code maintainability. A cleaner approach is to keep variables local to functions and pass data through parameters and return values. Small design decisions like this can make a big difference as projects grow. #Python #Programming #AI #DataScience #SoftwareEngineering #Coding #Instant

🚀 Day 16/30 – Python OOPs Challenge 💡 Operator Overloading in Python We learned that polymorphism means: 👉 Same name 👉 Different behaviour Today we’ll see how Python allows operators to behave differently. This is called Operator Overloading. 🔹 What is Operator Overloading? In Python: - + adds numbers - + also joins strings Same operator → different behaviour. We can also define how operators work for our own classes. 🔹 Example: ``` class Point: def __init__(self, x, y): self.x = x self.y = y def __add__(self, other): return Point(self.x + other.x, self.y + other.y) def display(self): print(self.x, self.y) p1 = Point(2, 3) p2 = Point(4, 5) p3 = p1 + p2 # Using overloaded + operator p3.display() ``` 🔹 What happened here? - We defined __add__() method - Now + works for Point objects - Python calls __add__() automatically 📌 Key takeaway: Special methods like __add__() allow operator overloading. 👉 Day 17: Method Overloading concept in Python (coming tomorrow) 👍 Like | 💬 Comment | 🔁 Share 📍 Follow me to learn Python OOP step by step #Python #OOP #LearningInPublic #30DaysOfPython #CodingJourney

Most Python code gets harder to read as the logic gets smarter. But not 𝗧𝗼𝗼𝗹𝘇 for Python. It gives you utility functions for iterators, functions, and dictionaries that work together naturally. Toolz feels especially relevant for data work, where clean transformations can save more time than any micro optimization. What I like most is that it stays pragmatic. Just simple Python functions that help you write code that is easier to understand without giving up performance. 🔗 Link to repo: github(.)com/pytoolz/toolz --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

🐍 Python’s mutable objects — what makes them tricky? They are objects whose state can change after creation. Here’s the real story: 1️⃣ Lists, sets, and dictionaries are mutable — you can add, remove, or update elements in place. 2️⃣ Strings, tuples, and numbers are immutable — any “change” creates a new object. 3️⃣ Mutability affects function arguments: passing a list means changes inside the function reflect outside too. 4️⃣ It also impacts default parameters — using a mutable default (like []) can cause unexpected sharing across calls. 5️⃣ Understanding mutability is key to debugging side effects and writing predictable code. Best practices: 1️⃣ Use immutable types when you want safety and consistency. 2️⃣ Be cautious with mutable defaults in function definitions. 3️⃣ Document clearly when functions modify inputs. 💡 The honest one-liner: In Python, mutability isn’t just about changing values — it’s about whether your changes ripple beyond where you expect. #Python